RSSI Map-Based Trajectory Design for UGV Against Malicious Radio Source : A Reinforcement Learning Approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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  • Zhaoyang Han
  • Yaoqi Yang
  • Lu Zhou
  • Thippa Reddy Gadekallu
  • Mamoun Alazab
  • Prosanta Gope
  • Chunhua Su

Related Research Unit(s)


Original languageEnglish
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Online published16 Dec 2022
Publication statusOnline published - 16 Dec 2022


Trajectory design is of great significance for the intelligent Unmanned Ground Vehicle (UGV) when performing various ground tasks. Though obstacle avoidance, speed control and other movement issues in the UGV navigation have been considered by the current research, the UGV path planning against malicious radio source is off the beaten path. To address such a research gap, we propose a reinforcement learning-based scheme to design UGV trajectory against malicious radio source as well as minimize the movement cost. Firstly, the malicious radio source detection and localization models are introduced after the Received Signal Strength Indicator (RSSI) map establishment. Then, the RSSI Map-based UGV trajectory design problem is formulated, where the movement cost and security risk are both concerned. To solve the formed problem, we propose a reinforcement learning-based trajectory design scheme, whose complexities are analyzed in detail. Finally, experiments are conducted under various parameter settings, where the simulation results evaluate the correctness and effectiveness of the proposed algorithm.

Research Area(s)

  • Computer science, Costs, Location awareness, malicious radio source, Optimization, Received signal strength indicator, reinforcement learning., RSSI map, Task analysis, Trajectory, trajectory design

Citation Format(s)